A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

Background/Objectives: At the time of acquisition and transmission noise is embedded with images. It introduces new but unwanted information (noise) in The elimination to analyze such data an essential step preprocessing. purpose this study propose a novel image denoising approach recover original images at high densities without introducing artifacts. Methods: A hybrid method based on approximation subband thresholding pre-Gaussian filtering presented study. Google Colab as platform python programming language used for implementation proposed technique. To evaluate performance Peak Signal Noise Ratio (PSNR) chosen. standard jpeg (Cameraman, Lena, Astronaut, Cat) have been taken input random different ratios (s =0.05,0.20,0.30,0.50) applied get noisy experiment. In scenarios, experimented grayscale images, compared existing methods. Findings: are denoised by method, quality calculated terms PSNR. results obtained from improve PSNR (PSNR= 25.80dB, s =0.50) levels significantly. Novelty: Gaussian filter However, when wavelet decomposition blended filtered band improved Hence, has wide area application field character recognition, agriculture, medical science, remote sensing. Keywords: Filter; Discrete Wavelet Thresholding; Image denoising; Processing